FHM: Faster High-Utility Itemset Mining Using Estimated Utility Co-occurrence Pruning

Fournier-Viger, Philippe; Wu, Cheng; Zida, Souleymane; Tseng, Vincent S.

doi:10.1007/978-3-319-08326-1_9

Cited by 336 publications

(259 citation statements)

References 10 publications

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“…Fournier-Viger, Wu, Zida, and Tseng (2014) proposed the Fast High-Utility Miner algorithm to extract all HUIs from a transaction database. While this is considered as an efficient approach, it generates a large number of HUIs, and this algorithm requires much space for processing as well as fails to terminate.…”

Section: High Utility Itemset Miningmentioning

confidence: 99%

An efficient approach for mining closed high utility itemsets and generators

Mai¹,

Nguyen

2017

Journal of Information and Telecommunication

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Mining closed high utility itemsets (CHUIs) serves as a compact and lossless representation of high utility itemsets (HUIs). CHUIs and their generators are useful in analytical and recommendation systems. In this paper, we introduce a lattice approach to extract CHUIs and their generators from a set of HUIs quickly. The experimental results show that mining CHUIs and their generators from a lattice of HUIs is efficient in both runtime and memory usage. ARTICLE HISTORY

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Section: High Utility Itemset Miningmentioning

confidence: 99%

An efficient approach for mining closed high utility itemsets and generators

Mai¹,

Nguyen

2017

Journal of Information and Telecommunication

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“…This is still suffering from large memory overhead. The FHM [22] algorithm occupies a large amount of memory space and it is time consuming too, because it computes all the candidate sets which makes it difficult to handle larger databases. In [23], IHUP recommends three novel data structures to efficiently perform incremental and interactive HUP mining.…”

Section: Related Workmentioning

confidence: 99%

“…The experiment is conducted by changing the minimum utility threshold value in percentage on ten thousand of the web transactions. The proposed algorithm {wp3} : ( 3, HUWSM is executed and compared with different existing algorithms FHM [22], IHUP [17], HUIMiner [25] algorithms. Fig.…”

Section: Experimental Evaluationmentioning

confidence: 99%

Jaccard Similarity based Mining for High Utility Webpage Sets from Weblog Database

Kumar¹,

Thakur²

2017

IJIES

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High utility webpage set states to those set of webpages which have high utility value in a weblog database. To find high utility item sets from transactional databases, there exist various algorithms. However, these existing algorithms mostly deals with data like-categorical, time series, binary etc. The weblog data is different from other types of data. The utility webpages extracted from the log data can be used for knowing the user's behaviour. In this research paper, two algorithms named HUWSM (high utility webpage sets mining) and HUWP-FP (high utility webpage sets -frequent pattern) Tree have been developed and used for efficiently mining high utility webpage sets from web log database. Along with this, a pattern generation technique based on the 'Jaccard Similarity' is also included in this method. The HUWSM method has also been compared with various other existing methods. This algorithm has shown a much better performance and more effectiveness over other algorithms like FHM, HUI-MINER and IHUP methods in terms of memory consumption and execution time.

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“…high profit), that is High-Utility Itemsets. HUIM has a wide range of applications [2,7,12]. The problem of HUIM is more difficult than the problem of FIM because the utility of an itemset is neither monotonic or anti-monotonic (a HUI may have a superset or subset with lower, equal or higher utility) [2,7,12].…”

Section: Introductionmentioning

confidence: 99%

“…Recently, to avoid the problem of candidate generation, the HUI-Miner [7] and d 2 HUP [8] algorithms were proposed to mine high-utility itemsets directly using a single phase. Then, improved versions of HUI-Miner named HUP-Miner [8] and FHM [2] were proposed, and are to our knowledge the current best algorithms for HUIM. However, despite all these research efforts, the task of high-utility itemset mining remains very computationally expensive.…”

Section: Introductionmentioning

confidence: 99%

EFIM: A Highly Efficient Algorithm for High-Utility Itemset Mining

Zida

Fournier-Viger

Lin

et al. 2015

Lecture Notes in Computer Science

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129

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Abstract. High-utility itemset mining (HUIM) is an important data mining task with wide applications. In this paper, we propose a novel algorithm named EFIM (EFficient high-utility Itemset Mining), which introduces several new ideas to more efficiently discovers high-utility itemsets both in terms of execution time and memory. EFIM relies on two upper-bounds named sub-tree utility and local utility to more effectively prune the search space. It also introduces a novel array-based utility counting technique named Fast Utility Counting to calculate these upper-bounds in linear time and space. Moreover, to reduce the cost of database scans, EFIM proposes efficient database projection and transaction merging techniques. An extensive experimental study on various datasets shows that EFIM is in general two to three orders of magnitude faster and consumes up to eight times less memory than the state-of-art algorithms d2 HUP, HUI-Miner, HUP-Miner, FHM and UP-Growth+.

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FHM: Faster High-Utility Itemset Mining Using Estimated Utility Co-occurrence Pruning

Cited by 336 publications

References 10 publications

An efficient approach for mining closed high utility itemsets and generators

An efficient approach for mining closed high utility itemsets and generators

Jaccard Similarity based Mining for High Utility Webpage Sets from Weblog Database

EFIM: A Highly Efficient Algorithm for High-Utility Itemset Mining

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